18th November 2023

nanoDPO v0.1 Release, a pioneering implementation of Direct Preference Optimization (DPO) for time series data, inspired by "Direct Preference Optimization: Your Language Model is Secretly a Reward Model," the cutting-edge DPO approach in language model fine-tuning.

Key Features:

• Causal Transformer and LSTM Integration: Incorporating Causal Transformer and LSTM models to handle time series data effectively.
• DPO Algorithm Implementation: Direct Preference Optimization for nuanced understanding and prediction of time series trends.
• DPO and Multi-Class Trainers: Two distinct training models catering to different time series analysis requirements.
• Customizable Training Configurations: Enhanced flexibility with adjustable learning rates, batch sizes, and model specifications.
• Robust performance metrics including accuracy and loss visualizations.
• Compatibility with popular machine learning tools like PyTorch and wandb.

Documentation:

For more information, visit the GitHub README and the detailed Documentation.